import os
import json
from typing import Any, Dict, Optional
from diffusers.models import UNet2DConditionModel

import numpy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
import torch.distributed
from PIL import Image
from einops import rearrange
from typing import Any, Callable, Dict, List, Optional, Union, Tuple

import diffusers
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    DiffusionPipeline,
    EulerAncestralDiscreteScheduler,
    UNet2DConditionModel,
    ImagePipelineOutput
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.models.attention_processor import Attention, AttnProcessor, XFormersAttnProcessor, AttnProcessor2_0
from diffusers.utils.import_utils import is_xformers_available


from diffusers.utils import deprecate

from diffusers.models.transformers.transformer_2d import BasicTransformerBlock



def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int):
    # "feed_forward_chunk_size" can be used to save memory
    if hidden_states.shape[chunk_dim] % chunk_size != 0:
        raise ValueError(
            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
        )

    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
    ff_output = torch.cat(
        [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
        dim=chunk_dim,
    )
    return ff_output


class Basic2p5DTransformerBlock(torch.nn.Module):
    def __init__(self, transformer: BasicTransformerBlock, layer_name, use_ma=True, use_ra=True) -> None:
        super().__init__()
        self.transformer = transformer
        self.layer_name = layer_name
        self.use_ma = use_ma
        self.use_ra = use_ra

        # multiview attn
        if self.use_ma:
            self.attn_multiview = Attention(
                query_dim=self.dim,
                heads=self.num_attention_heads,
                dim_head=self.attention_head_dim,
                dropout=self.dropout,
                bias=self.attention_bias,
                cross_attention_dim=None,
                upcast_attention=self.attn1.upcast_attention,
                out_bias=True,
            )

        # ref attn
        if self.use_ra:
            self.attn_refview = Attention(
                query_dim=self.dim,
                heads=self.num_attention_heads,
                dim_head=self.attention_head_dim,
                dropout=self.dropout,
                bias=self.attention_bias,
                cross_attention_dim=None,
                upcast_attention=self.attn1.upcast_attention,
                out_bias=True,
            )

    def __getattr__(self, name: str):
        try:
            return super().__getattr__(name)
        except AttributeError:
            return getattr(self.transformer, name)
        
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        timestep: Optional[torch.LongTensor] = None,
        cross_attention_kwargs: Dict[str, Any] = None,
        class_labels: Optional[torch.LongTensor] = None,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
    ) -> torch.Tensor:

        # Notice that normalization is always applied before the real computation in the following blocks.
        # 0. Self-Attention
        batch_size = hidden_states.shape[0]

        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
        num_in_batch = cross_attention_kwargs.pop('num_in_batch', 1)
        mode = cross_attention_kwargs.pop('mode', None)
        mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0)
        ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0)
        condition_embed_dict = cross_attention_kwargs.pop("condition_embed_dict", None)


        if self.norm_type == "ada_norm":
            norm_hidden_states = self.norm1(hidden_states, timestep)
        elif self.norm_type == "ada_norm_zero":
            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
            )
        elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
            norm_hidden_states = self.norm1(hidden_states)
        elif self.norm_type == "ada_norm_continuous":
            norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
        elif self.norm_type == "ada_norm_single":
            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
            ).chunk(6, dim=1)
            norm_hidden_states = self.norm1(hidden_states)
            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
        else:
            raise ValueError("Incorrect norm used")

        if self.pos_embed is not None:
            norm_hidden_states = self.pos_embed(norm_hidden_states)

        # 1. Prepare GLIGEN inputs
        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)

        attn_output = self.attn1(
            norm_hidden_states,
            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
            attention_mask=attention_mask,
            **cross_attention_kwargs,
        )

        if self.norm_type == "ada_norm_zero":
            attn_output = gate_msa.unsqueeze(1) * attn_output
        elif self.norm_type == "ada_norm_single":
            attn_output = gate_msa * attn_output

        hidden_states = attn_output + hidden_states
        if hidden_states.ndim == 4:
            hidden_states = hidden_states.squeeze(1)
        
        # 1.2 Reference Attention
        if 'w' in mode:
            condition_embed_dict[self.layer_name] = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch) # B, (N L), C

        if 'r' in mode and self.use_ra:
            condition_embed = condition_embed_dict[self.layer_name].unsqueeze(1).repeat(1,num_in_batch,1,1) # B N L C
            condition_embed = rearrange(condition_embed, 'b n l c -> (b n) l c')

            attn_output = self.attn_refview(
                norm_hidden_states,
                encoder_hidden_states=condition_embed,
                attention_mask=None,
                **cross_attention_kwargs
            )
            ref_scale_timing = ref_scale
            if isinstance(ref_scale, torch.Tensor):
                ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1)
                for _ in range(attn_output.ndim - 1):
                    ref_scale_timing = ref_scale_timing.unsqueeze(-1)
            hidden_states = ref_scale_timing * attn_output + hidden_states
            if hidden_states.ndim == 4:
                hidden_states = hidden_states.squeeze(1)
            

        # 1.3 Multiview Attention
        if num_in_batch > 1 and self.use_ma:
            multivew_hidden_states = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch)

            attn_output = self.attn_multiview(
                multivew_hidden_states,
                encoder_hidden_states=multivew_hidden_states,
                **cross_attention_kwargs
            )

            attn_output = rearrange(attn_output, 'b (n l) c -> (b n) l c', n=num_in_batch)

            hidden_states = mva_scale * attn_output + hidden_states
            if hidden_states.ndim == 4:
                hidden_states = hidden_states.squeeze(1)

        # 1.2 GLIGEN Control
        if gligen_kwargs is not None:
            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])

        # 3. Cross-Attention
        if self.attn2 is not None:
            if self.norm_type == "ada_norm":
                norm_hidden_states = self.norm2(hidden_states, timestep)
            elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
                norm_hidden_states = self.norm2(hidden_states)
            elif self.norm_type == "ada_norm_single":
                # For PixArt norm2 isn't applied here:
                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
                norm_hidden_states = hidden_states
            elif self.norm_type == "ada_norm_continuous":
                norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
            else:
                raise ValueError("Incorrect norm")

            if self.pos_embed is not None and self.norm_type != "ada_norm_single":
                norm_hidden_states = self.pos_embed(norm_hidden_states)

            
            attn_output = self.attn2(
                norm_hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                **cross_attention_kwargs,
            )

            hidden_states = attn_output + hidden_states

        # 4. Feed-forward
        # i2vgen doesn't have this norm 🤷‍♂️
        if self.norm_type == "ada_norm_continuous":
            norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
        elif not self.norm_type == "ada_norm_single":
            norm_hidden_states = self.norm3(hidden_states)

        if self.norm_type == "ada_norm_zero":
            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

        if self.norm_type == "ada_norm_single":
            norm_hidden_states = self.norm2(hidden_states)
            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp

        if self._chunk_size is not None:
            # "feed_forward_chunk_size" can be used to save memory
            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
        else:
            ff_output = self.ff(norm_hidden_states)

        if self.norm_type == "ada_norm_zero":
            ff_output = gate_mlp.unsqueeze(1) * ff_output
        elif self.norm_type == "ada_norm_single":
            ff_output = gate_mlp * ff_output

        hidden_states = ff_output + hidden_states
        if hidden_states.ndim == 4:
            hidden_states = hidden_states.squeeze(1)

        return hidden_states

import copy
class UNet2p5DConditionModel(torch.nn.Module):
    def __init__(self, unet: UNet2DConditionModel) -> None:
        super().__init__()
        self.unet = unet

        self.use_ma  = True
        self.use_ra  = True
        self.use_camera_embedding = True
        self.use_dual_stream = True

        if self.use_dual_stream:
            self.unet_dual = copy.deepcopy(unet)
            self.init_attention(self.unet_dual)
        self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra)
        self.init_condition()
        self.init_camera_embedding()

    
    @staticmethod
    def from_pretrained(pretrained_model_name_or_path, **kwargs):
        torch_dtype = kwargs.pop('torch_dtype', torch.float32)
        config_path = os.path.join(pretrained_model_name_or_path, 'config.json')
        unet_ckpt_path = os.path.join(pretrained_model_name_or_path, 'diffusion_pytorch_model.bin')
        with open(config_path, 'r', encoding='utf-8') as file:
            config = json.load(file)
        unet = UNet2DConditionModel(**config)
        unet = UNet2p5DConditionModel(unet)
        unet_ckpt = torch.load(unet_ckpt_path, map_location='cpu', weights_only=True)
        unet.load_state_dict(unet_ckpt, strict=True)
        unet = unet.to(torch_dtype)
        return unet

    def init_condition(self):
        self.unet.conv_in = torch.nn.Conv2d(
            12, 
            self.unet.conv_in.out_channels, 
            kernel_size=self.unet.conv_in.kernel_size, 
            stride=self.unet.conv_in.stride, 
            padding=self.unet.conv_in.padding, 
            dilation=self.unet.conv_in.dilation, 
            groups=self.unet.conv_in.groups, 
            bias=self.unet.conv_in.bias is not None)
        self.unet.learned_text_clip_gen = nn.Parameter(torch.randn(1,77,1024))
        self.unet.learned_text_clip_ref = nn.Parameter(torch.randn(1,77,1024))

    def init_camera_embedding(self):

        self.max_num_ref_image = 5
        self.max_num_gen_image = 12*3+4*2

        if self.use_camera_embedding:
            time_embed_dim = 1280
            self.unet.class_embedding = nn.Embedding(self.max_num_ref_image+self.max_num_gen_image, time_embed_dim)


    def init_attention(self, unet, use_ma=False, use_ra=False):

        for down_block_i, down_block in enumerate(unet.down_blocks):
            if hasattr(down_block, "has_cross_attention") and down_block.has_cross_attention:
                for attn_i, attn in enumerate(down_block.attentions):
                    for transformer_i, transformer in enumerate(attn.transformer_blocks):
                        if isinstance(transformer, BasicTransformerBlock):
                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, f'down_{down_block_i}_{attn_i}_{transformer_i}', use_ma, use_ra)
                            

        if hasattr(unet.mid_block, "has_cross_attention") and unet.mid_block.has_cross_attention:
            for attn_i, attn in enumerate(unet.mid_block.attentions):
                for transformer_i, transformer in enumerate(attn.transformer_blocks):
                    if isinstance(transformer, BasicTransformerBlock):
                        attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, f'mid_{attn_i}_{transformer_i}', use_ma, use_ra)

        for up_block_i, up_block in enumerate(unet.up_blocks):
            if hasattr(up_block, "has_cross_attention") and up_block.has_cross_attention:
                for attn_i, attn in enumerate(up_block.attentions):
                    for transformer_i, transformer in enumerate(attn.transformer_blocks):
                        if isinstance(transformer, BasicTransformerBlock):
                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, f'up_{up_block_i}_{attn_i}_{transformer_i}', use_ma, use_ra)


    def __getattr__(self, name: str):
        try:
            return super().__getattr__(name)
        except AttributeError:
            return getattr(self.unet, name)
        
    def forward(
        self, sample, timestep, encoder_hidden_states,
        *args, down_intrablock_additional_residuals=None,
        down_block_res_samples=None, mid_block_res_sample=None,
        **cached_condition,
    ):
        B, N_gen, _, H, W = sample.shape
        assert H == W

        if self.use_camera_embedding:
            camera_info_gen = cached_condition['camera_info_gen'] + self.max_num_ref_image
            camera_info_gen = rearrange(camera_info_gen, 'b n -> (b n)')
        else:
            camera_info_gen = None

        sample = [sample]
        if 'normal_imgs' in cached_condition:
            sample.append(cached_condition["normal_imgs"])
        if 'position_imgs' in cached_condition:
            sample.append(cached_condition["position_imgs"])
        sample = torch.cat(sample, dim=2)

        sample = rearrange(sample, 'b n c h w -> (b n) c h w')

        encoder_hidden_states_gen = encoder_hidden_states.unsqueeze(1).repeat(1, N_gen, 1, 1)
        encoder_hidden_states_gen = rearrange(encoder_hidden_states_gen, 'b n l c -> (b n) l c')

        if self.use_ra:
            if 'condition_embed_dict' in cached_condition:
                condition_embed_dict = cached_condition['condition_embed_dict']
            else:
                condition_embed_dict = {}
                ref_latents = cached_condition['ref_latents']
                N_ref = ref_latents.shape[1]
                if self.use_camera_embedding:
                    camera_info_ref = cached_condition['camera_info_ref']
                    camera_info_ref = rearrange(camera_info_ref, 'b n -> (b n)')
                else:
                    camera_info_ref = None
                
                ref_latents = rearrange(ref_latents, 'b n c h w -> (b n) c h w')

                encoder_hidden_states_ref = self.unet.learned_text_clip_ref.unsqueeze(1).repeat(B, N_ref, 1, 1)
                encoder_hidden_states_ref = rearrange(encoder_hidden_states_ref, 'b n l c -> (b n) l c')

                noisy_ref_latents = ref_latents
                timestep_ref = 0

                if self.use_dual_stream:
                    unet_ref = self.unet_dual
                else:
                    unet_ref = self.unet
                unet_ref(
                    noisy_ref_latents, timestep_ref,
                    encoder_hidden_states=encoder_hidden_states_ref,
                    class_labels=camera_info_ref,
                    # **kwargs
                    return_dict=False,
                    cross_attention_kwargs={
                        'mode':'w', 'num_in_batch':N_ref, 
                        'condition_embed_dict':condition_embed_dict},
                )
                cached_condition['condition_embed_dict'] = condition_embed_dict
        else:
            condition_embed_dict = None


        mva_scale = cached_condition.get('mva_scale', 1.0)
        ref_scale = cached_condition.get('ref_scale', 1.0)

        return self.unet(
            sample, timestep,
            encoder_hidden_states_gen, *args,
            class_labels=camera_info_gen,
            down_intrablock_additional_residuals=[
                sample.to(dtype=self.unet.dtype) for sample in down_intrablock_additional_residuals
            ] if down_intrablock_additional_residuals is not None else None,
            down_block_additional_residuals=[
                sample.to(dtype=self.unet.dtype) for sample in down_block_res_samples
            ] if down_block_res_samples is not None else None,
            mid_block_additional_residual=(
                mid_block_res_sample.to(dtype=self.unet.dtype)
                if mid_block_res_sample is not None else None
            ),
            return_dict=False,
            cross_attention_kwargs={
                'mode':'r', 'num_in_batch':N_gen, 
                'condition_embed_dict':condition_embed_dict, 
                'mva_scale': mva_scale,
                'ref_scale': ref_scale,
            },
        ) 